






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer

class 				lowerCAmelCase_	(		tf.keras.layers.Layer			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None      ):
											super().__init__()
											_lowercase							:   Optional[Any] 							=   pad_token_id
											_lowercase							:   Any 							=   max_length
											_lowercase							:   int 							=   vocab
											_lowercase							:   int 							=   merges
											_lowercase							:   Tuple 							=   BytePairTokenizer(_lowerCAmelCase ,						_lowerCAmelCase ,						sequence_length=_lowerCAmelCase      )


				@classmethod
				def 						__a					(					cls ,						_lowerCAmelCase ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											_lowercase							:   List[Any] 							=   [' '.join(_lowerCAmelCase      ) for m in tokenizer.bpe_ranks.keys()]
											_lowercase							:   Union[str, Any] 							=   tokenizer.get_vocab()
											return cls(_lowerCAmelCase ,						_lowerCAmelCase ,						*_lowerCAmelCase ,						**_lowerCAmelCase      )


				@classmethod
				def 						__a					(					cls ,						_lowerCAmelCase ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											_lowercase							:   int 							=   GPTaTokenizer.from_pretrained(_lowerCAmelCase ,						*_lowerCAmelCase ,						**_lowerCAmelCase      )
											return cls.from_tokenizer(_lowerCAmelCase ,						*_lowerCAmelCase ,						**_lowerCAmelCase      )


				@classmethod
				def 						__a					(					cls ,						_lowerCAmelCase      ):
											return cls(**_lowerCAmelCase      )


				def 						__a					(					self      ):
											return {
											    "vocab": self.vocab,
											    "merges": self.merges,
											    "max_length": self.max_length,
											    "pad_token_id": self.pad_token_id,
											}


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase = None      ):
											_lowercase							:   Tuple 							=   self.tf_tokenizer(_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   tf.ones_like(_lowerCAmelCase      )

											if self.pad_token_id is not None:
																		# pad the tokens up to max length
																		_lowercase							:   Optional[Any] 							=   max_length if max_length is not None else self.max_length

																		if max_length is not None:
																									_lowercase							,							_lowercase							:   Dict 							=   pad_model_inputs(
																									    _lowerCAmelCase ,						max_seq_length=_lowerCAmelCase ,						pad_value=self.pad_token_id      )

											return {"attention_mask": attention_mask, "input_ids": input_ids}









from typing import TYPE_CHECKING

from ...utils import _LazyModule


a      :  int			     =			{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
	from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
	import sys

	a      :  int			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






